Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
Arrow up icon
GO TO TOP
Mastering Python Design Patterns

You're reading from   Mastering Python Design Patterns A guide to creating smart, efficient, and reusable software

Arrow left icon
Product type Paperback
Published in Aug 2018
Publisher
ISBN-13 9781788837484
Length 248 pages
Edition 2nd Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Kamon Ayeva Kamon Ayeva
Author Profile Icon Kamon Ayeva
Kamon Ayeva
 Kasampalis Kasampalis
Author Profile Icon Kasampalis
Kasampalis
Arrow right icon
View More author details
Toc

Table of Contents (23) Chapters Close

Title Page
Copyright and Credits
Dedication
Packt Upsell
Contributors
Preface
1. The Factory Pattern FREE CHAPTER 2. The Builder Pattern 3. Other Creational Patterns 4. The Adapter Pattern 5. The Decorator Pattern 6. The Bridge Pattern 7. The Facade Pattern 8. Other Structural Patterns 9. The Chain of Responsibility Pattern 10. The Command Pattern 11. The Observer Pattern 12. The State Pattern 13. Other Behavioral Patterns 14. The Observer Pattern in Reactive Programming 15. Microservices and Patterns for the Cloud 1. Other Books You May Enjoy Index

Implementation


Python decorators are generic and very powerful. You can find many examples of how they can be used at the decorator library of python.org (j.mp/pydeclib). In this section, we will see how we can implement a memoization decorator (j.mp/memoi). All recursive functions can benefit from memoization, so let's try a function number_sum() that returns the sum of the first n numbers. Note that this function is already available in the math module as fsum(), but let's pretend it is not.

First, let's look at the naive implementation (the number_sum_naive.py file):

def number_sum(n): 
    '''Returns the sum of the first n numbers''' 
    assert(n >= 0), 'n must be >= 0' 

    if n == 0:
        return 0
    else:
        return n + number_sum(n-1)  

if __name__ == '__main__': 
    from timeit import Timer 
    t = Timer('number_sum(30)', 'from __main__ import number_sum')
    print('Time: ', t.timeit())

A sample execution of this example shows how slow this implementation is. It...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at £13.99/month. Cancel anytime
Visually different images